System and Method of Video Capture and Search Optimization

ABSTRACT

The system and method of the present application captures video of a scene in accordance with a plurality of capture characteristics, generates a capture profile for the video, and creates an index of the captured profile to enable a more rapid search of the captured video.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority of U.S. Provisional Patent Application No. 61/667,547, filed Jul. 3, 2012, the content of which is incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present application is related to the field of video display and management systems, and in particular, to optimizing the searching of surveillance video.

TECHNICAL BACKGROUND

Effective video surveillance requires high quality images that can be viewed, searched, and analyzed. In many circumstances, images for people who are and were in an area may need to be known and identified, for viewing, searching and analyzing. Such video is often taken of various areas, and searching volumes of this video for particular individuals may be time consuming.

SUMMARY

The system and method of the present application captures video of a scene in accordance with a plurality of capture characteristics, generates a capture profile for the video, and creates an index of the captured profile to enable a more rapid search of the captured video.

In one embodiment, a non-transitory computer readable medium has stored thereon instructions for optimizing video surveillance that, when executed by a video system, direct the video system to identify a boundary in a scene, capture video of the scene, wherein the scene comprises objects, associate target objects with the boundary, create an index of the captured video, and search the captured video for the target objects based at least in part on the index.

In another embodiment, a computerized, method of optimizing video surveillance comprises identifying a boundary in an area, capturing video of the area including a target object, analyzing the captured video of a scene of the area to generate an index for the video when the target object is adjacent the boundary, and searching for the target Object in the captured video based at least in part on the index.

In yet another embodiment, a system may be provided which includes a capture interface configured to capture video of a scene including a target object and a predefined boundary object, and a processing system coupled with the capture interface and configured to analyze the captured video of a scene of the area to generate an index for the video when the target object is adjacent the boundary, and searching for the target object in the captured video based at least in part on the index.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding, parts throughout the several views. While several embodiments are described in connection with these drawings, the disclosure is not limited to the embodiments disclosed herein. On the contrary, the intent is to cover all alternatives, modifications, and equivalents.

FIG. 1 illustrates a block diagram of an embodiment of a video analysis system of the present application;

FIG. 2 illustrates a flow diagram of an embodiment of a method of operation of a video analysis system of the present application;

FIG. 3 illustrates a schematic diagram of an embodiment of a video surveillance environment of the present application;

FIG. 4 illustrates a flow diagram of an embodiment of a method of operation of a video analysis module of the present application;

FIG. 5 illustrates a flow diagram of an embodiment of operations in a surveillance environment.

FIG. 6 illustrates a graphical user interface of an embodiment of the present application.

FIG. 7 illustrates a graphical user interface of an embodiment of the present application.

DETAILED DESCRIPTION

In the present description, certain terms have been used for brevity, clearness and understanding. No unnecessary limitations are to be applied therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes only and are intended to be broadly construed. The different systems and methods described herein may be used alone or in combination with other systems and methods. Various equivalents, alternatives and modifications are possible within the scope of the appended claims. Each limitation in the appended claims is intended to invoke interpretation under 35 U.S.C. §112, sixth paragraph, only if the terms “means for” or “step for” are explicitly recited in the respective limitation.

FIG. 1 illustrates video analysis system 101. Video analysis system 101 includes processing system 103, video interface 105, memory 107, and video analysis module 109. Video analysis system 101 may be implemented in a number of ways, such as within or as part of a video recorder, a video camera, a client device, or as a stand-alone system. Video analysis system 101 operates as further described below to monitor and maintain the quality of captured video in surveillance settings. It should be noted that the processing system 103 of FIG. 1 is capable of executing computer software and/or code in order to effectuate the operation of the system 101, and any other processor method disclosed in this application.

Referring to FIG. 2, process 200 is shown. Process 200 describes the operation of video analysis system 101. To begin, processing system 103 associates a target object with a boundary (step 210). The boundary may be defined by a user through the system. The boundary may be a vertical or horizontal straight line placed over the scene captured by the camera, or may be a curved or other line defined by a user through a user interface. The boundary may be an area in a scene or room, such that a video camera may capture a clear image of a face of a person in the scene. The face shot, head shot, or other shot may be the target object. The target object may therefore be any defined object, as defined by a user in the scene.

When a person crosses the boundary, processing system 103 may index where in the captured video the target object (e.g. face shot) is located. Later when face shots are queried by a user, the system can quickly search for all of the captured target objects (face shots). This greatly reduce the time needed to search the captured video for identification of persons who are or were in the area. If the target object is not found in the search of the indexed captured video, the rest of the captured video may be searched.

Processing system 103 may obtain or otherwise access the video by way of video interface 105, which itself may interface to a variety of video sources, such as a camera feed, stored video, or other video sources.

Next, processing system 103 creates an index of the captured video (step 220). The index may be the portion of the captured video where a clear face shot is present within the scene captured.

Finally, processing system 103 searches the captured video, and using the index, can quickly search the captured videos to identify people who are or were in the area (step 230). It should be noted that the system and method of the present application is not by itself a face recognition application, but a system and method that will index target objects (in this case face shots) for identification by another or another system. Of course, such a face recognition system could indeed be utilized in conjunction with, or be added to, the system and method of the present application. This may be performed in any number of ways, depending upon how video analysis system 101 is deployed.

FIG. 3 illustrates video surveillance environment 300 in another embodiment. Video surveillance environment 300 includes video capture device (VCD) 301, VCD 303, and VCD 305. VCDs 301, 303 and 305 communicate over video network 307 with recorder 309 and client device 311. As shown, VCD 301 generally captures video of scene A, which includes target object 313 and object 315. Likewise, VCD 303 generally captures video of scene B, which includes target object 317 and object 315. VCD 305 captures video of scene C, which includes target object 321 and object 315.

VCD 301 is shown with video analysis module (VAM) 331, the operation of which will be described in more detail below with respect to FIG. 3. VAM 331 is capable of associating target object 313, 317, 321 with the boundary object 315 and creating an indication where within the captured video the target object is shown. VAM 331 is capable of searching the captured video quickly, only searching the captured video that have target objects 313, 317, 321. In this manner, search times for face shots of people who are or were in the area may be greatly reduced.

It should be understood that VAM 331 could be implemented within VCD 303 and VCD 305, as well as within VCD 301. It should also be understood that similar video analysis modules could be implemented within other elements of video surveillance environment 300, such as with recorder 309, client device 311, within other element on video network 307 not shown, or as a stand-alone element. VAM 311 may be implemented as a software component, a hardware component, firmware, or any combination thereof.

Client device 311 may be capable of allowing a user to define the boundary. This is then used by VAM 331, such that when a person crosses the boundary, the general position or time within the captured video where this took place can be indexed and analyzed. VAM 331 may then search the captured video, in this example, stored on recorder 309 to quickly find a target object 313, 317, 321.

Referring again to FIG. 4, a graphical depiction of the operation of VAM 331 is provided. Only a single video analysis module is shown handling video of scenes A, B, and C, although it should be understood that, depending upon the chosen architecture, multiple video analysis modules 331 may be involved.

Continuing with FIG. 4, VAM 331 receives video of scenes A, B, and C. As shown to the left of VAM 331, the video initially video captured by VCD 301 of scene A includes objects 313 and 315. VAM 331 generates an index 350 or any other indication that an object (a person) is adjacent or has crossed another object 315 (the boundary). VAM 331 may then associate the objects, and create an index 350 or list or other notation of where a search may find a relatively good image of the objects.

Still referring to FIG. 4, VAM 331 produces results similar to those of scene A respect to scene B and scene C when the same operations are performed. As shown to the left of VAM 331, objects 317 and 321 represent different persons. The location in the captured video when the person crossed the boundary is noted for faster searching later when needed or desired.

FIG. 5 illustrates several different implementations, each an example of a deployment of VAM 331. Various data flows are described relating to the various implementations of VAM 331. It should be understood that FIG. 5 is merely illustrative and non-limiting and does not encompass all the possible deployments of VAM 331.

In the first implementation represented by the solid line, VAM 331 is implemented in VCD 301. It should be understood this example could apply as well to VCD 303 and VCD 305. In this situation. VCD 301 executes VAM 331 to associate potential target objects 313, 317, 319 with a boundary 315, note where in the captured video that occurs, and be capable of relatively rapidly searching the captured video for a target object. After searching, the target object(s) 313, 317, 319 may be displayed on client station 311. The captured video to be searched may resides on recorder 309 or any other location capable of performing that function.

As represented by the dashed line in FIG. 5, VAM 331 could also be implemented in recorder 309. In this case, unmodified video is initially transferred from VCD 301 to recorder 309. An alternative, recorder 309 could implement the analyzing, and indexing itself, as opposed to instructing VCD 301 to make the modifications. In a final example, VAM 311 is implemented in client device 311, as represented by the dotted lines. In this case, unmodified video is initially transferred from VCD 301 to client device 311.

Referring now both to FIGS. 6 and 7, a graphical user interface (GUI) 500 is illustrated. It should be noted that this GUI 500 is exemplary of the system and method of the present application, and that other embodiments and screen shots could also be utilized to illustrate the workings of the system and method of the present application. Here, an exemplary image search screen 504 is illustrated including index images 502 from the system and method described above. These indexed images 502 include both the indexed image 512 as well as selective buttons 514. FIG. 6 illustrates the results of an index search conducted by a user, resulting in the found indexed images 502. A user may utilize the selective buttons 514 in order to play the selected piece of video corresponding to the image 512, or to remove the image 512, or to add the image 512 to the storyboard 506. The user may do that with all of the indexed images 502 found in an index search. The storyboard 506 allows a user to collect a number of index images 502 and put them in order to follow a particular individual through a number of different indexed images 502, for example. The image search screen 504 also includes camera folders 508 that organize a large number of video capture devices and a camera list 510 for each of the camera folders 508. Referring specifically to FIG. 7, the image search screen 504 includes a search query menu 516 that allows the user to set a particular and specific set of search criteria that will produce the indexed images 502 of FIG. 6. Again, the search query menu 516 as well as the image search screen 504 generally, may be organized, embodied and/or implemented in a number of different ways, and the GUI 500 of FIGS. 6 and 7 are not intended to be limiting to this particular embodiment.

The included descriptions and figures depict specific embodiments to teach those skilled in the an how to make and use the best mode. For the purpose of teaching inventive principles, some conventional aspects have been simplified or omitted. Those skilled in the art will appreciate variations from these embodiments that fall within the scope of the invention. Those skilled in the art will also appreciate that the features described above can be combined in various ways to form multiple embodiments. As a result, the invention is not limited to the specific embodiments described above, but only by the claims and their equivalents.

In the foregoing description, certain terms have been used for brevity, clearness, and understanding. No unnecessary limitations are to be inferred therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes and are intended to be broadly construed. The different configurations, systems, and method steps described herein may be used alone or in combination with other configurations, systems and method steps. It is to be expected that various equivalents, alternatives and modifications are possible within the scope of the appended claims. 

What is claimed is:
 1. A non-transitory computer readable medium having stored thereon instructions for optimizing video capture and search that, when executed by a video system, direct the video system to: identify a boundary in a scene; capture video of the scene, wherein the scene comprises objects; associate target objects in the captured video with the boundary; create an index of the captured video; and search the captured video for the target objects based at least in part on the index.
 2. The computer readable medium of claim 1 wherein the target object comprises a face, head shape, or person.
 3. The computer readable medium of claim 1 wherein the video system comprises a camera that captured the video.
 4. The computer readable medium of claim 1 wherein the objects comprise the boundary.
 5. The computer readable medium of claim 1 further comprising determining if the target object was in the scene.
 6. The computer readable medium of claim 5 further comprising searching more captured video if it is determined the target object was not in the scene.
 7. A computerized method of optimizing video capture, the method comprising: identifying a boundary in an area capturing video of the area including a target object; analyzing, the captured video of a scene of the area to generate an index for the video when the target object is adjacent the boundary; and searching for the target object in the captured video based at least in part on the index.
 8. The method of claim 7 wherein search time is relative reduced using the index.
 9. The method of claim 7 wherein the target object comprises a face, head shape, or person.
 10. The method of claim 7 wherein the video system comprises a camera that captured the video.
 11. The method of claim 7 wherein the objects comprise the boundary.
 12. The method of claim 7 further comprising determining if the target object was in the scene.
 13. The method of claim 12 further comprising searching more captured video if it is determined the target object was not in the scene.
 14. A video capture system comprising: a capture interface configured to capture video of a scene including a target object and a predefined boundary object; and a processing system coupled with the capture interface and configured to analyze the captured video of the scene of the area to generate an index for the video when the target object is adjacent the predefined boundary object, and searching for the target object in the captured video based at least in pan on the index.
 15. The system of claim 14 wherein the target object comprises a face, head shape, or person.
 16. The system of claim 4 wherein the capture system comprises a camera that captured the video.
 17. The system of claim 14 further comprising determining if the predefined target object was in the scene.
 18. The system of claim 17 further comprising searching more captured video if it is determined the predefined target object was not in the scene. 